Nonstationary Rainfall Frequency Estimates for Texas

@AGU 2023, NH14B-07

JAMES DOSS-GOLLIN

Rice University
Civil & Environmental Engineering

Yuchen Lu (Rice)

Benjamin Seiyon Lee (GMU)

John Nielsen-Gammon (TAMU)

Rewati Niraula (TWDB)

…for better or for worse,

IDF CURVES UNDERPIN RISK ASSESSMENT

Houston Chronicle

Bates et al. (2021) fig. 8

Mark Wolfe/FEMA News

EXISTING GUIDANCE LEAVES GAPS

CLIMATE IS CHANGING BUT SAMPLING VARIABILITY CHALLENGES TREND ESTIMATION

Fagnant et al. (2020): each line is a gauge from the same \(5^\circ \times 3^\circ\) region

JUST USE ESMS?

Sample many realizations of weather to overcome sampling variability

Physically constrained; better extrapolate to future climates

Drizzle bias and dynamical limitations motivate downscaling / bias correction sampling variability is back in the picture!

NONSTATIONARY MODELS

NEED MORE PARAMETERS

Generic nonstationary model for annual maximum precipitation: \[ y(\mathbf{s}, t) \sim \text{GEV} \left( \mu(\mathbf{s}, t), \sigma(\mathbf{s}, t), \xi(\mathbf{s}, t) \right) \]

Process-informed models condition parameters on climate indices \(\mathbf{X}(t)\) (Cheng & AghaKouchak, 2014; Schlef et al., 2023) \[ \theta(\mathbf{s}, t) = \alpha + \underbrace{\sum_{j=1}^J \beta(\mathbf{s}) \mathbf{X}(t)}_{\text{nonstationarity model}\atop\text{more parameters}} \]

NONSTATIONARY MODELS

INCREASE ESTIMATION UNCERTAINTY

Serinaldi & Kilsby (2015): more parameters, same data more uncertainty

TWDB-FUNDED RICE-TAMU COLLAB

MORE/BETTER DATA

We use long-record gauges AND newer mesonets

🤝

SPATIALLY VARYING COVARIATES

  • framework: Bayesian hierarchical model (flexible, probabilistic)
  • hypothesis: parameters are smooth
  • model: latent parameters as spatial fields (Moran basis functions)

POSITIVE TRENDS FOR MOST OF TEXAS

WE FIND HIGHER HAZARD THAN ATLAS-14 EXCEPT IN HARVEY-IMPACTED AREAS

WELL-CALIBRATED EXTREME PROBABILITIES

SUMMARY

Our Bayesian space-time model:

Represents medium-term changes to extreme precipitation probabilities

Reduces estimation uncertainty

Demonstrates well-calibrated inferences

Explicitly spatial (free interpolation)

Overcomes sampling variability

References

Bates, P. D., Quinn, N., Sampson, C., Smith, A., Wing, O., Sosa, J., et al. (2021). Combined modeling of US fluvial, pluvial, and coastal flood hazard under current and future climates. Water Resources Research, 57(2), e2020WR028673. https://doi.org/10.1029/2020WR028673
Cheng, L., & AghaKouchak, A. (2014). Nonstationary precipitation intensity-duration-frequency curves for infrastructure design in a changing climate. Scientific Reports, 4(1), 7093. https://doi.org/10.1038/srep07093
Fagnant, C., Gori, A., Sebastian, A., Bedient, P. B., & Ensor, K. B. (2020). Characterizing spatiotemporal trends in extreme precipitation in Southeast Texas. Natural Hazards, 104(2), 1597–1621. https://doi.org/10.1007/s11069-020-04235-x
Schlef, K. E., Kunkel, K. E., Brown, C., Demissie, Y., Lettenmaier, D. P., Wagner, A., et al. (2023). Incorporating non-stationarity from climate change into rainfall frequency and intensity-duration-frequency (IDF) curves. Journal of Hydrology, 616, 128757. https://doi.org/10.1016/j.jhydrol.2022.128757
Serinaldi, F., & Kilsby, C. G. (2015). Stationarity is undead: Uncertainty dominates the distribution of extremes. Advances in Water Resources, 77, 17–36. https://doi.org/10.1016/j.advwatres.2014.12.013

@jdossgollin